Short answer: To filter a list of lists for a condition on the inner lists, use the list comprehension statement
[x for x in list if condition(x)] and replace
condition(x) with your filtering condition that returns
True to include inner list
Lists belong to the most important data structures in Python—every master coder knows them by heart! Surprisingly, even intermediate coders don’t know the best way to filter a list—let alone a list of lists in Python. This tutorial shows you how to do the latter!
Problem: Say, you’ve got a list of lists. You want to filter the list of lists so that only those inner lists remain that satisfy a certain condition. The condition is a function of the inner list—such as the average or sum of the inner list elements.
Example: Given the following list of lists with weekly temperature measurements per week—and one inner list per week.
# Measurements of a temperature sensor (7 per week) temperature = [[10, 8, 9, 12, 13, 7, 8], # week 1 [9, 9, 5, 6, 6, 9, 11], # week 2 [10, 8, 8, 5, 6, 3, 1]] # week 3
How to filter out the colder weeks with average temperature value <8? This is the output you desire:
print(cold_weeks) # [[9, 9, 5, 6, 6, 9, 11], [10, 8, 8, 5, 6, 3, 1]]
If you’re short on time, you can also get a quick overview by playing with the code in your web browser—I’ll explain the code after that.
Method 1: List Comprehension
The most Pythonic way of filtering a list—in my opinion—is the list comprehension statement
[x for x in list if condition]. You can replace
condition with any function of
x you would like to use as a filtering condition. Only elements that are in the
list and meet the
condition are included in the newly created list.
Solution: Here’s how you can solve the above problem to filter a list of lists based on a function of the inner lists:
# Measurements of a temperature sensor (7 per week) temperature = [[10, 8, 9, 12, 13, 7, 8], # week 1 [9, 9, 5, 6, 6, 9, 11], # week 2 [10, 8, 8, 5, 6, 3, 1]] # week 3 # How to filter weeks with average temperature <8? # Method 1: List Comprehension cold_weeks = [x for x in temperature if sum(x)/len(x)<8] print(cold_weeks) # [[9, 9, 5, 6, 6, 9, 11], [10, 8, 8, 5, 6, 3, 1]]
The second and third list in the list of lists meet the condition of having an average temperature of less than 8 degrees. So those are included in the variable
You can visualize the memory usage of this code snippet in the following interactive tool:
This is the most efficient way of filtering a list and it’s also the most Pythonic one. If you look for alternatives though, keep reading.
- List Comprehension — Python List of Lists
- Filter() vs List Comprehension
- Nested List Comprehension
- The Ultimate Guide to Python Lists
- List Comprehension
Method 2: Filter() Function
filter(function, iterable) function takes a function as input that takes on argument (a list element) and returns a Boolean value that indicates whether this list element should pass the filter. All elements that pass the filter are returned as a new
iterable object (a filter object).
You can use the
lambda function statement to create the function right where you pass it as an argument. The syntax of the lambda function is
lambda x: expression and it means that you use
x as an input argument and you return expression as a result (that can or cannot use
x to decide about the return value). For more information, see my detailed blog article about the lambda function.
# Measurements of a temperature sensor (7 per week) temperature = [[10, 8, 9, 12, 13, 7, 8], # week 1 [9, 9, 5, 6, 6, 9, 11], # week 2 [10, 8, 8, 5, 6, 3, 1]] # week 3 # How to filter weeks with average temperature <8? # Method 2: Map() cold_weeks = list(filter(lambda x: sum(x) / len(x) < 8, temperature)) print(cold_weeks) # [[9, 9, 5, 6, 6, 9, 11], [10, 8, 8, 5, 6, 3, 1]]
Again, the second and third list in the list of lists meet the condition of having an average temperature of less than 8 degrees. So those are included in the variable
filter() function returns a filter object that’s an
iterable. To convert it to a list, you use the
Play with this code by clicking “Next” in the interactive code visualization tool:
- How to Filter Using the Lambda Function
- Which is Faster – List Comprehension or Map()?
- How to Filter Using the Lambda Function?
- Python Programming Tutorial
Where to Go From Here?
Enough theory. Let’s get some practice!
Coders get paid six figures and more because they can solve problems more effectively using machine intelligence and automation.
To become more successful in coding, solve more real problems for real people. That’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?
You build high-value coding skills by working on practical coding projects!
Do you want to stop learning with toy projects and focus on practical code projects that earn you money and solve real problems for people?
🚀 If your answer is YES!, consider becoming a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.
If you just want to learn about the freelancing opportunity, feel free to watch my free webinar “How to Build Your High-Income Skill Python” and learn how I grew my coding business online and how you can, too—from the comfort of your own home.
While working as a researcher in distributed systems, Dr. Christian Mayer found his love for teaching computer science students.
To help students reach higher levels of Python success, he founded the programming education website Finxter.com. He’s author of the popular programming book Python One-Liners (NoStarch 2020), coauthor of the Coffee Break Python series of self-published books, computer science enthusiast, freelancer, and owner of one of the top 10 largest Python blogs worldwide.
His passions are writing, reading, and coding. But his greatest passion is to serve aspiring coders through Finxter and help them to boost their skills. You can join his free email academy here.